This paper studies the problem of event-triggered (ET) fault detection (FD) for nonlinear systems via multi-degree-delay-dependent Lyapunov function (LF). The nonlinearity in the system conforms to an incremental quadratic constraint, representing a broader class of nonlinearities. First, a nonlinear FD observer is designed to work in tandem with the ET mechanism for generating residual signals. Subsequently, by employing a LF that includes multi-degree-delay-dependent Lyapunov matrix, it is proved that the established nonlinear FD observer can guarantee the desired performance of the nonlinear FD systems. Finally, simulation results are presented to illustrate that the proposed ET FD approach outperforms existing methods in terms of FD effectiveness.

This paper investigates the problem of path tracking for intelligent connected vehicles subjected to limited communication bandwidth, unknown parameters, and nonzero sideslip angles, and an adaptive event-triggered fixed-time path tracking control strategy is proposed based on the bound estimation method and smooth functions. Different from the existing control methods, the path tracking problem is first converted into a yaw angle tracking problem, where the sideslip angle compensation is incorporated into the desired yaw angle for realizing a more accurate path tracking maneuver. Then, an improved dynamic event-triggered mechanism (ETM) is designed to schedule the signal transmission intervals. Unlike traditional ETMs, the non-monotonic internal dynamic variable is designed to achieve a better tradeoff between resource utilization and system performance. Third, the lower bounds of unknown control coefficients are incorporated into the Lyapunov function to eliminate the requirement of vehicle parameter identification. Moreover, the prescribed performance constraints, formulated based on the time-varying nature of vehicle stability margins, are taken into account for the yaw stability and the transient performance improvement. Finally, the effectiveness of the proposed control method is validated by experimental results.

In this article, mixed equilibrium problems (MEPs) in uncertain and dynamic environments are investigated by employing a multiagent system (MAS), where each agent only has access to its own bifunction, and can communicate with its immediate neighbors via a time-varying digraph. At each time, the goal of agents is to cooperatively find a point in the constraint set such that the sum of local bifunctions with a free variable is nonnegative. Different from existing works on MEPs, here the bifunctions are stochastic and time-varying, and only available to agents after decisions are made. To tackle this problem, an online distributed learning algorithm based on the mirror descent algorithm and the clipping strategy is proposed. The dynamic regret, whose offline benchmark is to find the solution at each time, is employed to measure the performance of the algorithm. Of particular interest is that the high probability bound of the regret is established under the heavy-tailed noise condition. The result shows that if the variation in the solution sequence is within a certain range, then the dynamic regret grows sublinearly with high probability. Finally, a simulation example is provided to corroborate the validity of the theoretical results.

This article proposes a novel nonsingleton try-once-discard (TOD) scheduling scheme for networked control systems (NCSs) under the semantic-driven communication, aimed at avoiding node collisions while simultaneously enhancing system operational efficiency. First, a research framework is established for NCSs operating within the semantic-driven communication paradigm. Second, a method for semantic extraction based on natural language is introduced. Subsequently, the "cultural clash" between semantic interactions and bitstream-based system components is addressed through the application of fuzzy mathematical methods. Under such semantic-driven communication mechanism, raw data is successfully compressed. Building upon the efficient compression of data, a nonsingleton TOD scheduling scheme is presented. In comparison to the traditional TOD scheduling approaches that activate only one node per transmission, the proposed scheduling scheme facilitates the simultaneous transmission of information from multiple nodes, thereby enhancing system efficiency greatly. By data-modeling semantic disparities and designing the appropriate controller, the input-to-state stability of the studied system is guaranteed, even when confronted with certain levels of semantic discrepancies. Moreover, an algorithm is provided to optimize semantically-related parameters, so as to reduce semantic discrepancies as much as possible. Finally, the effectiveness of the proposed method is verified by a six-area power system.

The quadratic program (QP) with equality constraint is widely involved in science and engineering fields. Numerous solutions to the equality-constrained QP (ECQP) have been reported, particularly the zeroing neural network (ZNN) for the time-dependent ECQP. However, such solutions can be severely affected by the harmonic noise and may lose their efficacy. This study aims to address the above limitation by proposing the new ZNN model against harmonic noise with the only known frequency. Such a model, called the harmonic noise rejection ZNN (HNR-ZNN) model, is established by incorporating the dynamics of the harmonic signal (from which the unknown information for the signal's amplitude and phase can be eliminated). Theoretical analysis indicates that the proposed HNR-ZNN model effectively determines the optimal solution of time-dependent ECQP under harmonic noise interference. Comparative computer simulations and real-world robot applications further indicate the validity, excellence, and practicality of the presented HNR-ZNN model.

In this article, the problem of piecewise fault detection (FD) for continuous-time piecewise linear (PWL) system with measurement outliers via new event-triggered (ET) mechanisms is investigated. First, piecewise FD observer with adaptive saturation of output errors is designed to mitigate the effect of outliers on the residual signals, such that false alarms are avoided through the established FD scheme. Then, based on the presented piecewise FD observer, a new ET design scheme that depends on the different regions where the states of the PWL system and piecewise FD observer are located is proposed, and an ET FD method is studied through the ET mechanisms. Unlike the existing results, the established FD scheme can effectively avoid false alarm caused by outliers, and the piecewise FD observer is designed separately from the new ET mechanisms, while co-design is necessary in the existing results. Finally, the effectiveness of the presented scheme is demonstrated by simulation examples.

Autonomous marine vehicles, which provide a platform for the successful implementation of special tasks, such as maritime rescue, maritime measurement, and dangerous goods monitoring, have been widely utilized. In the last decade, considerable attention has been paid to the analysis, modeling, and networked control of autonomous marine vehicles. This survey provides recent advances in networked and DRL-based control for autonomous marine vehicles. Typical mathematical models of autonomous marine vehicles are introduced first as the foundation for control of autonomous marine vehicles. Then, networked and DRL-based control for autonomous marine vehicles is reviewed. Finally, some challenges and open issues are presented to motivate the future research.

To achieve fault diagnosis and prognosis, obtaining adequate and reliable life-cycle data is essential. However, this poses a challenge in current high-reliable Internet of Things (IoT) systems. Fortunately, accelerated degradation testing (ADT) can be employed to overcome this hurdle. Nevertheless, a dependable testing and measuring technique is required to construct an accurate model for ADT. This testing method plays a vital role in evaluating fault diagnosis, prognosis, lifetime, and maintenance decisions for reliable products under operational stress. To ensure effective testing, it is crucial to utilize appropriate models that account for the individual heterogeneity of products. However, the commonly used single stochastic models in ADT overlook the impact of this condition in real-world applications, resulting in misspecification problem. To address this limitation, we propose a novel mixed stochastic process model that integrates multi-Wiener processes and dynamic weights. In addition, we leverage interval analysis to analyze system lifetime, considering the limited data size. The estimation of unknown parameters in our mixed model is achieved using the Metropolis–Hastings algorithm. By analyzing stress relaxation data from electrical connectors, we demonstrate the superior accuracy of our mixed model over conventional single stochastic models in ADT.

In this article, a distributed active disturbance rejection formation tracking control strategy is developed for a quadrotor unmanned aerial vehicle (UAV) swarm system with switching communication topologies. The proposed control strategy consists of two parts: 1) attitude-loop control and 2) position-loop control. First, a distributed cascade active disturbance rejection control (CADRC) method is designed for the attitude subsystem. With this attitude control method, the stability of the attitude subsystem can be achieved even in the presence of unknown time-varying disturbances. Second, a distributed formation tracking control method is designed for the position subsystem. This position control method ensures that the quadrotor UAV swarm maintains dynamic formation flying and accurately tracks a predetermined trajectory. Through stability analysis, it can be proved that the proposed control strategy can ensure the stability of the whole swarm system. Finally, the proposed control strategy is applied to a quadrotor UAV swarm system to verify its effectiveness and the ability to suppress the influence of unknown time-varying disturbances.

The product and its supply chain (SC) are two key parts of corporate competitiveness under mass production or customization. Nevertheless, they are frequently isolated in manufacturing field evolution. At the operational decision level, this article studies uncertainty processing in the coevolution of products and SCs using a measure-to-control supported approach. A decision system framework that can incorporate macrochange interactions between products and SCs is presented. From the systematic view on coevolution, an interface for measure and control of uncertainty propagation is provided, which has two potential benefits: 1) facilitating concurrent measures of risks with multiple causes and consequences; and 2) allowing control theory to tackle multiple dynamics and identify their interrelations. Specifically, via effective value and entropy rate measures of bullwhip effect and ripple effect, cost-guaranteed control methods are proposed for attenuating risks of the product-SC uncertainty system. Finally, a mask SC application verifies the robustness and resilience of the proposed approach.

This article is concerned with the leader–follower formation control problem of multiple networked mobile robots (MRs), where the information exchanges over communication networks among the MRs suffer from replay attacks. The central aim is to develop an effective secure control scheme for the multiple-MR system such that the desired leader–follower formation control objectives are accomplished regardless of the simultaneous presence of replay attacks, network-induced delays, system uncertainties, and external disturbances. Toward this aim, an extended state observer is first constructed to estimate the unknown nonlinear terms consisting of system uncertainties and external disturbances. Then, leveraging the time-stamp technique, a dedicated data packet analyzer is developed for each MR to detect and handle replay attacks. Furthermore, a secure leader–follower formation control scheme, consisting of a network-based cooperative kinematic control law and two local kinetic control laws, is designed. Finally, both simulation and experiment results are provided to validate the efficacy of the proposed secure leader–follower formation control scheme.

This paper addresses the incipient fault detection and isolation (FDI) problem for high-speed rail vehicle suspension systems and explores further results of data-driven total measurable fault information residual (ToMFIR) with Takagi-Sugeno (T-S) fuzzy dynamical mode. Firstly, the T-S fuzzy model is constructed to represent the global nonlinear dynamics of a China Railway High-speed (CRH) trailer car. Secondly, the data-driven ToMFIR based on T-S fuzzy theory and system identification is designed with the help of system input/output(I/O) data models. Moreover, Jensen-Shannon (JS) divergence based evaluation function is proposed for monitoring the slight changes of data-driven ToMFIR. Furthermore, generalized likelihood ratio (LR) reconstruction method combined with data-driven ToMFIR is designed for incipient isolation of suspension actuator and sensor faults. Finally, simulation studies conducted on SIMPACK-MATLAB/Simulink Co-simulation environment are given to demonstrate the effectiveness of the developed FDI scheme for both slowly developing faults and incipient faults with intermittent characteristics.

This article concentrates on proposing a scalable deep reinforcement learning (DRL) method for a multiple unmanned surface vehicle (multi-USV) system to operate cooperative target invasion. The multi-USV system, which is made up of multiple invaders, needs to invade target areas in a specified time. A novel scalable reinforcement learning (RL) method called Scalable-MADDPG is proposed for the first time. In this method, the scale of the multi-USV system can be changed at any time without interrupting the training process. Then, to mitigate the policy oscillation after applying Scalable-MADDPG, a bi-directional long-short-term memory (Bi-LSTM) network is constructed. Moreover, an improved -greedy strategy is proposed to help balance the exploration and exploitation in RL. Furthermore, to enhance the robustness of the optimal policy, Ornstein-Uhlenbeck (OU) noise is added in this improved -greedy strategy during the training process. Finally, the scalable RL method is used to help the multi-USV system perform cooperative target invasion under complex marine environments. The effectiveness of Scalable-MADDPG is demonstrated through three experiments.

For mass production or mass customization, changes in the product supply chain network (PSCN) have risks on the market competitiveness of most corporations that produce differentiated products. This article focuses on the PSCN operational risk mitigation strategy supported by distributed control. First, the bullwhip effect (BE) with operational and behavioral causes is analyzed and quantified, which is more general than considering one-cause only. Then, a distributed control strategy is designed to attenuate the BE, which has more modularity and robustness potential than using centralized and decentralized control. The main results are derived to show the existence of inventory controllers. Finally, a case study examines the effectiveness of the presented strategy.

In this article, the problem of decentralized event-triggered fault-tolerant control (FTC) for a class of interconnected nonlinear systems with unknown strong coupling and actuator failures is considered. In order to enable each subsystem output to track the desired trajectory, a new decentralized adaptive control scheme is given. First, an event-triggering mechanism is introduced to reduce the signal transmission frequency between the controller and the actuator. Second, a fuzzy high-gain observer is designed for each subsystem to estimate unknown nonlinear functions and actuator efficiency factor. Third, a decentralized FTC strategy is proposed to compensate for the effects of actuator failures and achieve the desirable system tracking performance. With the aid of graph theory, it is shown that all the closed-loop signals are semiglobally uniformly ultimately bounded, and the tracking error of each subsystem can converge to an arbitrarily small residual set by adjusting a design parameter. The effectiveness of the proposed scheme is demonstrated by a practical interconnected system.

This article is concerned with event-triggered dynamic positioning for a mass-switched unmanned marine vehicle (UMV) in network environments. First, a switched dynamic positioning system (DPS) model for a mass-switched marine vehicle is established. The switched DPS model takes into consideration changes in the marine vehicle's mass and the resultant switching of the marine vehicle's parameters. Second, for a mass-switched UMV controlled through a communication network, a novel weighted event-triggering communication scheme considering switching features is proposed. The weighted error data of multiple sampling instants are utilized to avoid a long-time nontriggering phenomenon. The consideration of switching features guarantees the current sampled data to be transmitted if a switch occurs between the last sampling instant and the current sampling instant. Then, under the event-triggering scheme, an asynchronously switched DPS model for the mass-switched UMV is established in network environments. Based on this model, a mode-dependent DPS controller and event generator co-design method are proposed to attenuate the disturbance induced by wind, waves, and ocean currents. The DPS performance analysis demonstrates the effectiveness of the proposed method.

This article investigates the load frequency control (LFC) problem for smart grids with denial-of-service (DoS) attacks via a novel event-triggered control method. In order to sustain the stable operation of the smart grids, a handshake logic-based event-triggered communication scheme is proposed. The targets of the designed controller are twofold. First, an event-triggered mechanism with a handshake logic-based concept is proposed to save network resources. In this triggering method, a handshake protocol is used to detect DoS attacks in the communication networks. Second, by employing Lyapunov stability theory, sufficient stabilization criteria ensuring the uncertain LFC systems being asymptotically stable are deduced. Based on these conditions, the corresponding state-feedback controller design method is given. Finally, a simulation of a two-area power system with electric vehicles is given as an example to substantiate the proposed method of this article.

This article is concerned with the$H_{\infty }$cluster formation control of a multiagent system (MAS) with stochastic sampling in network environments. First, based on a directed communication topology with acyclic partition, agents are separated into several clusters. The agents moving in the same cluster are expected to achieve a desired formation collaboratively, while the agents in different clusters have different formation patterns. Second, the external disturbance for each agent is taken into account. The associated$H_{\infty }$cluster formation control, whose performance bound can be obtained by considering both formation information and cluster characteristics, is introduced to measure the disturbance attenuation ability. Third, by casting a stochastic sampled-data-based$H_{\infty }$cluster formation problem into an$H_{\infty }$control problem of a stochastic system, an$H_{\infty }$cluster formation criterion is derived for the MAS with external disturbance. Finally, a lower-dimensional cluster formation criterion is obtained for the disturbance-free MAS. Cluster formation performance analysis testifies the effectiveness of the proposed design methods.

For the output feedback control problem of a Takagi-Sugeno (T-S) fuzzy system with unknown premise variables (UPV) and external disturbances, a new observer-based controller H∞robust synthesis approach is proposed. To utilize the membership functions more effectively, in this article, a membership functions constructed vector is used as new premise variables. Then, the error items which are induced by UPV are decoupled into a new T-S fuzzy system. For the derived stability and robustness conditions in complex bilinear matrix inequality form, more relaxed sufficient conditions which can be solved directly by convex optimization method are proposed. Finally, a suspended floater control system based simulation process is carried out. The conservatism reduction effects among traditional and decoupling approaches are given, the state estimation effects of the observer and control performances are also verified.

This article investigates resilient event-triggered load frequency control (LFC) of multiarea power systems under nonideal network environments. Under the sample-data framework, a novel QoS-dependent event-triggered communication (QEC) scheme is presented to deal with nonideal network environments while preserving the desired control performance and improving the communication efficiency. In comparison with some traditional state-dependent event-triggered communication schemes, since the proposed QEC depends not only on the state of controlled plant but also on the QoS of communication network, higher communication efficiency can be achieved. Then, a resilient LFC is well developed based on the proposed QEC, where “resilient” implies that: 1) for a normal QoS case, less packets are transmitted to save the communication resources and 2) for an abnormal QoS case, more packets are transmitted to mitigate the influence of nonideal QoS. Moreover, the proposed method provides a better way to balance the control performance and communication resource by reasonably choosing the parameters of QEC. Finally, the simulation results show the effectiveness of the proposed method.

In this paper, the cooperative dynamic positioning (CDP) of multiple unmanned surface vehicles (multi-USVs) is studied in network environments. Firstly, networked cooperative dynamic positioning system (CDPS) models are constructed for multi-USVs subject to disturbance induced by wind, waves, and current. Secondly, considering the negative influences of network-induced delays, a CDP control protocol is designed. Then, by employing Lyapunov-Krasovskii approach, a controller design criterion ensuring CDP is derived such that the errors of earth-fixed position and heading between unmanned surface vehicles (USVs) and a reference state vector converge to a bounded region. Finally, the performance analysis verifies the effectiveness of the design approach presented in this paper.

Distributed coordination control is the current trend in networked systems and finds prosperous applications across a variety of fields, such as smart grids and intelligent transportation systems. One fundamental issue in coordinating and controlling a large group of distributed and networked agents is the influence of intermittent interagent interactions caused by constrained communication resources. Event-triggered communication scheduling stands out as a promising enabler to strike a balance between the desired control performance and the satisfactory resource efficiency. What distinguishes dynamic event-triggered scheduling from traditional static event-triggered scheduling is that the triggering mechanism can be dynamically adjusted over time in accordance with both available system information and additional dynamic variables. This article provides an up-to-date overview of dynamic event-triggered distributed coordination control. The motivation of dynamic event-triggered scheduling is first introduced in the context of distributed coordination control. Then some techniques of dynamic event-triggered distributed coordination control are discussed in detail. Implementation and design issues are well addressed. Furthermore, this article exemplifies two applications of dynamic event-triggered distributed coordination control in the fields of microgrids and automated vehicles. Several challenges are suggested to direct the future research.

Smart grids (SGs), which can be classified into a class of networked distributed control systems, are designed to deliver electricity from various plants through a communication network to serve individual consumers. Due to the complexity of environments, the distribution of the spatial locations and vulnerability of the communication networks, cyber security emerges to be a critical issue because millions of electronic devices are interconnected via communication networks throughout critical power facilities. This paper addresses a comprehensive security understanding of the SGs framework, attacks scenarios, detection/protection methods, estimation and control strategies from both communication and control viewpoints. Also, some potential challenges and solution approaches are discussed to deal with the threat issues of SGs. At last, some conclusions and highlight future research directions are presented.

This paper is concerned with a Takagi-Sugeno (T-S) fuzzy dynamic positioning controller design for an unmanned marine vehicle (UMV) in network environments. Network-based T-S fuzzy dynamic positioning system (DPS) models for the UMV are first established. Then, stability and stabilization criteria are derived by taking into consideration an asynchronous difference between the normalized membership function of the T-S fuzzy DPS and that of the controller. The proposed stabilization criteria can stabilize states of the UMV. The dynamic positioning performance analysis verifies the effectiveness of the networked modeling and the controller design.

This paper investigates the event-triggered predictive control problem for networked nonlinear systems with imperfect premise matching. First, a model of networked nonlinear system is well constructed, which has integrated the event-triggered communication scheme (ETCS) and the predictive control together, in which, an ETCS is introduced to alleviate the communication burden by reducing the number of transmitted packets; and a fuzzy predictive controller is designed to predict future states and control signals between two successfully transmitted instants. Therefore, the data dropout induced by the networks can be actively compensated. Second, by using a common Lyapunov theory, a stability criterion and two stabilization criteria are deduced to ensure the asymptotical stability of the studied system and find the controller gains, respectively. Different from the traditional parallel distributed compensation method, the synchronous premise variables between the T-S fuzzy system and the fuzzy event-triggered predictive controller (FETPC) are no longer needed again. Since the imperfect premise matching condition is well considered in the derivation of the main results, the design flexibility and low cost of the FETPC implementation can be expected. Finally, the validity of the method proposed in this paper is demonstrated by a nonlinear mass-spring example.

The observer-based controller design for a T-Sfuzzy system subject to external disturbance is investigated in this paper. First, an H∞ observer is designed to estimate the unknown system states, by a transformation process for system states and the disturbance term, a state feedback H∞ controller is given. Then, the separation principle for a T-S fuzzy system subject to external disturbance is proposed. With the help of the improved separation principle, the observer and controller can be combined together to make the fuzzy system input-state stable, while the H∞ index can also be guaranteed. The proposed design approach is applicable for both known and unknown premise variables. In stability analysis, the membership functions derivatives dependent method is proposed to reduce the conservatism. In the simulation part, stability analysis of a three-rule system is carried out to illustrate the conservatism reduction effects in Example 1; then, a vessel dynamic positioning control process subject to ocean disturbance is adopted to illustrate the effectiveness of the proposed methods for both known/unknown premise variable cases.

This paper studies the problem of adaptively adjusted event-triggering mechanism-based fault detection for a class of discrete-time networked control system (NCS) with applications to aircraft dynamics. By taking into account the fault occurrence detection progress and the fault occurrence probability, and introducing an adaptively adjusted event-triggering parameter, a novel event-triggering mechanism is proposed to achieve the efficient utilization of the communication network bandwidth. Both the sensor-to-control station and the control station-to-actuator network-induced delays are taken into account. The event-triggered sensor and the event-triggered control station are utilized simultaneously to establish new network-based closed-loop models for the NCS subject to faults. Based on the established models, the event-triggered simultaneous design of fault detection filter (FDF) and controller is presented. A new algorithm for handling the adaptively adjusted event-triggering parameter is proposed. Performance analysis verifies the effectiveness of the adaptively adjusted event-triggering mechanism, and the simultaneous design of FDF and controller.

This paper investigates the adaptively adjusted event-triggering mechanism-based fault detection for a discrete-time networked control system (NCS). The fault occurrence detection progress, the fault occurrence probability, and an adaptively adjusted event-triggering parameter are taken into account to construct a novel event-triggering mechanism. The event-triggered sensor and the event-triggered control station are used simultaneously to establish new network-based closed-loop models for the NCS subject to an actuator fault, and the sensor-to-control station and the control station-to-actuator network-induced delays. Based on the established models, the event-triggered simultaneous design of a fault detection filter and a controller is presented. A new algorithm for handling the adaptively adjusted event-triggering parameter is proposed. We analyse the performance of the simultaneous design of a fault detection filter and a controller to verify its effectiveness.

This paper studies the problem of event-triggered fault detection filter (FDF) and controller coordinated design for a continuous-time networked control system (NCS) with biased sensor faults. By considering sensor-to-FDF network-induced delays and packet dropouts, which do not impose a constraint on the event-triggering mechanism, and proposing the simultaneous network bandwidth utilization ratio and fault occurrence probability-based event-triggering mechanism, a new closed-loop model for the considered NCS is established. Based on the established model, the event-triggered H∞performance analysis, and FDF and controller coordinated design are presented. The combined mutually exclusive distribution and Wirtinger-based integral inequality approach is proposed for the first time to deal with integral inequalities for products of vectors. This approach is proved to be less conservative than the existing Wirtinger-based integral inequality approach. The designed FDF and controller can guarantee the sensitivity of the residual signal to faults and the robustness of the NCS to external disturbances. The simulation results verify the effectiveness of the proposed event-triggering mechanism, and the FDF and controller coordinated design.

This paper is concerned with the network-based modeling, and observer-based fault detection filter (FDF) and controller coordinated design for an unmanned surface vehicle (USV) in network environments. Network-based models for the USV subject to actuator faults and wave-induced disturbances are established for the first time by introducing an observer-based FDF, and considering network-induced characteristics such as delays and packet dropouts in the sampler-to-control station communication network channel and the control station-to-actuator communication network channel. Based on these models, network-based FDF and controller coordinated design criteria are derived to asymptotically stabilize the residual system. The designed network-based FDF and controller can guarantee the sensitivity of the residual signal to faults and the robustness of the USV to external disturbances. Fault detection performance analysis verifies the effectiveness of the proposed network-based FDF and controller coordinated design scheme for the USV in network environments.